Network Based Kernel Density Estimator                for Urban Dynamics Analysis                Nicolas Lachance-Bernard,...
Content •    Context and definitions •    Barcelona: Conceptualization and computation •    Ljubljana: Volunteered Geograp...
Context •     Problematic        – Fact          Spatial events location influenced by environment forms        – Principa...
Network based Kernel Density Estimator                       KDE                                         NetKDE           ...
Case study data 2009-2011                               Barcolona         Ljubljana           Geneva                Baghda...
Barcelona: Conceptualization and computation •     Comparing NetKDE and KDE approaches        – Model effects?        – Gr...
Barcelona case: Model & Bandwidth variations (200m)                   KDE 400m     KDE 600m               KDE 800m        ...
Barcelona case: Grid resolution variations (200m)                                                    H                   K...
Barcelona: Model variations very high resolution grid (10m)                                                    H        KD...
Barcelona: Conceptualization and computation •    Calculation        – 9 computers / +500 hours •    Conclusion (2009-2010...
Ljubljana: VGI and GPS tracking density •    Criteria for urban planning decision making        – Where to build infrastru...
NetKDE (Left) KDE (Right) results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m)                        Data Tier: L...
KDE results                                                                      20m grid                                 ...
NetKDE                                                                         results                                    ...
Ljubljana: VGI and GPS tracking density •    Calculation        – 1 computer / NetKDE 77 hours (- 50%) KDE 18 hours (- 80%...
Geneva: Visualization and clustering •    Spatial cluster in the city        – Where are located the activities?        – ...
Geneva: Visualization and clustering •    Calculation        NOT Disclosed •    Conclusion (June 2011)      New approaches...
Baghdad: Spatio-temporal multi-dimension analysis NOT DisclosedNLB / 12.09.11 / p.18                         Network Based...
Baghdad: Spatio-temporal multi-dimension analysis                        NetKDE 800                                 NetKDE...
Conclusion •    Spatio-temporal monitoring of urban dynamics        – Developed methodology for analysis of land uses cons...
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Network Based Kernel Density Estimator for Urban Dynamics Analysis

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Network Based Kernel Density Estimator for Urban Dynamics Analysis

  1. 1. Network Based Kernel Density Estimator for Urban Dynamics Analysis Nicolas Lachance-Bernard, Timothée Produit, Loïc Gasser, Stéphane Joost and François Golay Geographic Information Systems Laboratory, Ecole polytechnique fédérale de Lausanne Environmental Engineering Institute Green Days 2011 September 8th – 9th 2011, Champoussin, SwitzerlandNLB / 12.09.11 / p.1 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  2. 2. Content • Context and definitions • Barcelona: Conceptualization and computation • Ljubljana: Volunteered Geographic Information (VGI) and GPS tracking • Geneva: Visualization and clustering • Baghdad: Spatio-temporal multi-dimension analysis • Further research Cover picture: Photograph NLB, New city of Belval, Luxembourg, 2011NLB / 12.09.11 / p.2 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  3. 3. Context • Problematic – Fact Spatial events location influenced by environment forms – Principal need Adapting density estimation for graph constrained space – Goal Spatio-temporal monitoring of urban dynamics • Challenges 1. To use very large datasets 2. To decrease processing times compared to out-of-the-box 3. To develop multi-scale approaches 4. To develop visualization methodsNLB / 12.09.11 / p.3 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  4. 4. Network based Kernel Density Estimator KDE NetKDE Source: Produit et Lachance-Bernard 2009, 2010NLB / 12.09.11 / p.4 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  5. 5. Case study data 2009-2011 Barcolona Ljubljana Geneva Baghdad (2009/2010) (May 2011) (June 2011) (June 2011) Network (Agency) 20 km2 (OSM) (Agency) (OSM) Segments 11,200 8,100 10,800 66,600 Events Eco. Activities Cyclist GPS Eco. Activities War records Locations 166,300 314,200 15,000 93,100 Types 1 1 12 16 + Weights Grid 200m 100m 100m 20m 200m 50m 50m 20m 10m - Gridpoints 160,000 310,000 300,000 Buildings - - 70,000 - Bandwidths [20m,6000m] [40m,1000m] - KDE (Steps) (20m) (20m) Bandwidths [100m,1000m] 60m 100m 500m NetKDE (Steps) (100m) 200m 400m 9 computers 1 computer 1 computer 1 computer Processing time 500h 18h + 77h 15h & 20h & KDE + NetKDE 5min/class 5min/classNLB / 12.09.11 / p.5 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  6. 6. Barcelona: Conceptualization and computation • Comparing NetKDE and KDE approaches – Model effects? – Grid resolution effects? – Bandwidth effects? • Data and Parameters – Retail and service activities (166,311 events) – Street network (11,222 segments) – Grids 200m, 100m, 50m, 20m, 10m (+160,000 gridpoints) – Bandwidths: NetKDE [100m, 1000m], KDE [20m, 6000m]NLB / 12.09.11 / p.6 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  7. 7. Barcelona case: Model & Bandwidth variations (200m) KDE 400m KDE 600m KDE 800m KDE 1000m NetKDE 400m NetKDE 600m NetKDE 800m NetKDE 1000m Low density High density Not calculatedNLB / 12.09.11 / p.7 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  8. 8. Barcelona case: Grid resolution variations (200m) H KDE grid: 200m, band.: 500m NetKDE grid: 200m, band.: 500m L KDE grid: 50m, band.: 500m NetKDE grid: 50m, band.: 500mNLB / 12.09.11 / p.8 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  9. 9. Barcelona: Model variations very high resolution grid (10m) H KDE grid: 10m, band.: 500m (ZOOM) NetKDE grid: 10m, band.: 500m (ZOOM) L KDE grid: 10m, band.: 500m NetKDE grid: 10m, band.: 500mNLB / 12.09.11 / p.9 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  10. 10. Barcelona: Conceptualization and computation • Calculation – 9 computers / +500 hours • Conclusion (2009-2010) Important further R&D needs – Multi-scale, multi-resolution, comparison/clustering methods – Optimization of NetKDE/KDE algorithmsNLB / 12.09.11 / p.10 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  11. 11. Ljubljana: VGI and GPS tracking density • Criteria for urban planning decision making – Where to build infrastructures considering current behaviors? – Which are the most important locations of use? – Are VGI data reliable? • Data and Parameters – GPS tracking (314,250 points) – OpenStreetMap network (8,114 segments) – Grids 100m, 20m  20km2 (310,000 gridpoints) – Bandwidths: NetKDE 60m, 100m, 200m, 400m KDE [40m, 1000m]NLB / 12.09.11 / p.11 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  12. 12. NetKDE (Left) KDE (Right) results 20m grid (Bandwidths: a-60m; b-100m; c-200m; d-400m) Data Tier: Ljubljana and VGINLB / 12.09.11 / p.12 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  13. 13. KDE results 20m grid Bandwidths: A)60m B)100m C)200m D)400m *Deciles distributionNLB / 12.09.11 / p.13 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  14. 14. NetKDE results 20m grid Bandwidths: A)60m B)100m C)200m D)400m *Deciles distributionNLB / 12.09.11 / p.14 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  15. 15. Ljubljana: VGI and GPS tracking density • Calculation – 1 computer / NetKDE 77 hours (- 50%) KDE 18 hours (- 80%) • Conclusion (2011) Ready to be used by urban planning professional – Infrastructure development: • Level of use corridor • Most active intersection • Constrained area or behaviourNLB / 12.09.11 / p.15 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  16. 16. Geneva: Visualization and clustering • Spatial cluster in the city – Where are located the activities? – Is there a hierarchy or structure present between activities? – What is the best way to represent urban densities? • Data and Parameters NOT DisclosedNLB / 12.09.11 / p.16 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  17. 17. Geneva: Visualization and clustering • Calculation NOT Disclosed • Conclusion (June 2011) New approaches to understand cities – Planning: City and economic development, urban sprawl – Tech: New visualization, fastest algorithms, density clustering methodsNLB / 12.09.11 / p.17 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  18. 18. Baghdad: Spatio-temporal multi-dimension analysis NOT DisclosedNLB / 12.09.11 / p.18 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  19. 19. Baghdad: Spatio-temporal multi-dimension analysis NetKDE 800 NetKDE 1400NLB / 12.09.11 / p.19 Network Based Kernel Density Estimator for Urban Dynamics Analysis
  20. 20. Conclusion • Spatio-temporal monitoring of urban dynamics – Developed methodology for analysis of land uses constrained by transportation network – Looking for spatio-temporal trends, hotspots, axes, flux, … (What? Where? Who? When? How? …) • Proof-of-concept – Barcelona: playing with scale and model How does scale change vision? – Ljubljana: biking around the city Where are people most active? – Geneva: choosing the right place to develop Where are the density and the diversity? – Baghdad: playing with time and classification When and how do event density patterns change?NLB / 12.09.11 / p.20 Network Based Kernel Density Estimator for Urban Dynamics Analysis

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